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I moved everyday drafting and editing from Grammarly to a local language model because I wanted sensitive writing processed on my own computer. With a downloaded model and local inference, the text sent to that model can stay on the device. That does not mean every writing-related activity—or every network connection from the laptop—is local: downloads, update checks, cloud models, web search, and other apps have different boundaries.
Why I moved writing away from Grammarly
I wanted a workflow for drafts I would rather not send to an online writing service: personal notes, early-stage work, and text that did not need cloud-based collaboration. A local LLM offered a way to ask for rewrites or feedback while keeping the inference step on my computer.
That is a narrower claim than saying the laptop never sends writing anywhere. It describes what happens when a particular app processes text with a downloaded local model. It does not automatically cover other apps, browser extensions, integrations, or optional online features.
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Grammarly says it needs access to text while a Grammarly product is actively being used, such as its browser extension, so it can provide feedback. Its privacy page says its algorithms analyze text, writing behavior, usage data, and other information to provide suggestions and related services. Documents saved in Grammarly Editor remain stored until the user deletes them or the account. Grammarly Privacy Policy
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Grammarly’s support FAQ adds that other text is processed as needed to deliver and administer the service. It says Grammarly may use random samples of aggregated, de-identified user content to improve its products unless a user opts out of product improvement and training. For generative AI features, Grammarly says inputs may be shared with service providers, but those providers are not allowed to train on user content. Grammarly also says it does not sell user content. These are the company’s descriptions of its practices, not an independent audit. Grammarly Support: How Grammarly uses your data
So the privacy decision was not based on a claim that Grammarly sells writing or lets its AI providers train on it. It was about choosing a different processing boundary for the text I preferred to keep on my machine.
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What “local LLM” means in this workflow
A local model runner downloads model files and runs inference on the computer rather than sending each prompt to a hosted model. Once the model is installed, the prompt, response, and any documents processed locally can remain on the device. Whether that is true for a given request depends on the selected model and features: choosing a cloud model or enabling web search changes the boundary.
LM Studio
LM Studio’s Desktop App Privacy Policy, effective June 2026, says messages, chat histories, and documents processed with downloaded local models are not transmitted from the system. It identifies model searches and downloads, along with app update checks, as network-connected activities. Cloud models and web search are separate cloud services that receive requests when used. The policy says, “LM Studio processes as little info as possible, and can run entirely on-device and offline.” LM Studio Desktop App Privacy Policy
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Ollama
Ollama’s Privacy Policy, last updated March 2026, says locally processed prompts, responses, interactions, and content are not collected, stored, transmitted, or accessible to Ollama. It says limited device and usage metadata may be collected, excluding prompt and response content; model download metadata is one example. Cloud-hosted model use is a separate mode in which prompts and responses are processed transiently. The policy’s phrase “Your data stays on your machine” refers to locally processed content, not every network function or cloud mode. Ollama Privacy Policy
Where text can still cross the boundary
The local model is only one part of a writing setup. The relevant question is not simply which app you installed, but which component receives a particular piece of text and what it does with it.
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- Model selection: A downloaded local model can process text on-device; selecting a cloud-hosted model sends requests to that service.
- Optional features: Web search and other cloud functions may send requests beyond the local model runner.
- Setup and maintenance: Finding and downloading models requires network access. LM Studio also describes app update checks as network activity. Those connections are distinct from local inference.
- Other writing tools: A browser extension, office integration, or another app may have access to text independently of the local model. Review what is active in the apps where you write.
- Saved data: Check whether the writing app stores documents or chat histories locally, remotely, or both; the fact that inference is local does not answer how every app stores its own data.
What changed—and what did not
The main change is control over where the model processes a prompt: with a downloaded local model, I can use an inference path that does not send that text to a model provider. The tradeoff is that local operation requires obtaining and managing model files, and it does not include cloud features unless I choose to use them.
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Privacy policies establish what the vendors say about their systems; they do not establish my laptop’s actual network traffic or prove that a particular setup is configured as intended. Nor do the cited policies compare correction quality, speed, or convenience. Those depend on the model, the task, and the user’s workflow, so I would test the replacement on ordinary non-sensitive writing before relying on it for important work.
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Checklist for keeping a local writing workflow local
- Install the model before handling sensitive text. Model search and downloads are network activities; after acquisition, select the downloaded model for local inference.
- Check the model and feature before each task. Avoid cloud-hosted models and web search when you need the text to stay on-device.
- Review app and extension access. Disable writing extensions or integrations you do not need in the editor, browser, or other apps where the text appears.
- Understand storage separately from inference. Check the app’s settings and policy for saved documents, conversations, and histories, then delete anything you do not want retained.
- Allow for maintenance traffic. Model downloads and update checks may connect to the internet even when text inference is local. If offline operation matters, finish setup first and review the app’s network behavior and available controls.
This checklist is workflow guidance based on the vendors’ stated boundaries, not a security certification. “Local” is accurate for text processed by a local model under those conditions—not a blanket guarantee about everything written or every connection made by a laptop.
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